7,337 research outputs found
A Genetic Programming Approach to Designing Convolutional Neural Network Architectures
The convolutional neural network (CNN), which is one of the deep learning
models, has seen much success in a variety of computer vision tasks. However,
designing CNN architectures still requires expert knowledge and a lot of trial
and error. In this paper, we attempt to automatically construct CNN
architectures for an image classification task based on Cartesian genetic
programming (CGP). In our method, we adopt highly functional modules, such as
convolutional blocks and tensor concatenation, as the node functions in CGP.
The CNN structure and connectivity represented by the CGP encoding method are
optimized to maximize the validation accuracy. To evaluate the proposed method,
we constructed a CNN architecture for the image classification task with the
CIFAR-10 dataset. The experimental result shows that the proposed method can be
used to automatically find the competitive CNN architecture compared with
state-of-the-art models.Comment: This is the revised version of the GECCO 2017 paper. The code of our
method is available at https://github.com/sg-nm/cgp-cn
Incorporating characteristics of human creativity into an evolutionary art algorithm
A perceived limitation of evolutionary art and design algorithms is that they rely on human intervention; the artist selects the most aesthetically pleasing variants of one generation to produce the next. This paper discusses how computer generated art and design can become more creatively human-like with respect to both process and outcome. As an example of a step in this direction, we present an algorithm that overcomes the above limitation by employing an automatic fitness function. The goal is to evolve abstract portraits of Darwin, using our 2nd generation fitness function which rewards genomes that not just produce a likeness of Darwin but exhibit certain strategies characteristic of human artists. We note that in human creativity, change is less choosing amongst randomly generated variants and more capitalizing on the associative structure of a conceptual network to hone in on a vision. We discuss how to achieve this fluidity algorithmically
Incorporating characteristics of human creativity into an evolutionary art algorithm (journal article)
A perceived limitation of evolutionary art and design algorithms is that they rely on human intervention; the artist selects the most aesthetically pleasing variants of one generation to produce the next. This paper discusses how computer generated art and design can become more creatively human-like with respect to both process and outcome. As an example of a step in this direction, we present an algorithm that overcomes the above limitation by employing an automatic fitness function. The goal is to evolve abstract portraits of Darwin, using our 2nd generation fitness function which rewards genomes that not just produce a likeness of Darwin but exhibit certain strategies characteristic of human artists. We note that in human creativity, change is less choosing amongst randomly generated variants and more capitalizing on the associative structure of a conceptual network to hone in on a vision. We discuss how to achieve this fluidity algorithmically
A Hybrid Differential Evolution Approach to Designing Deep Convolutional Neural Networks for Image Classification
Convolutional Neural Networks (CNNs) have demonstrated their superiority in
image classification, and evolutionary computation (EC) methods have recently
been surging to automatically design the architectures of CNNs to save the
tedious work of manually designing CNNs. In this paper, a new hybrid
differential evolution (DE) algorithm with a newly added crossover operator is
proposed to evolve the architectures of CNNs of any lengths, which is named
DECNN. There are three new ideas in the proposed DECNN method. Firstly, an
existing effective encoding scheme is refined to cater for variable-length CNN
architectures; Secondly, the new mutation and crossover operators are developed
for variable-length DE to optimise the hyperparameters of CNNs; Finally, the
new second crossover is introduced to evolve the depth of the CNN
architectures. The proposed algorithm is tested on six widely-used benchmark
datasets and the results are compared to 12 state-of-the-art methods, which
shows the proposed method is vigorously competitive to the state-of-the-art
algorithms. Furthermore, the proposed method is also compared with a method
using particle swarm optimisation with a similar encoding strategy named IPPSO,
and the proposed DECNN outperforms IPPSO in terms of the accuracy.Comment: Accepted by The Australasian Joint Conference on Artificial
Intelligence 201
Cartesian genetic programming for trading: a preliminary investigation
In this paper, a preliminary investigation of Cartesian Genetic Programming (CGP) for algorithmic intraday trading is conducted. CGP is a recent new variant of genetic programming that differs from traditional approaches in a number of ways, including being able to evolve programs with limited size and with multiple outputs. CGP is used to evolve a predictor for intraday price movements, and trading strategies using the evolved predictors are evaluated along three dimensions (return, maximum drawdown and recovery factor) and against four different financial datasets (the Euro/US dollar exchange rate and the Dow Jones Industrial Average during periods from 2006 and 2010). We show that CGP is capable in many instances of evolving programs that, when used as trading strategies, lead to modest positive returns
Kartezio: Evolutionary Design of Explainable Pipelines for Biomedical Image Analysis
An unresolved issue in contemporary biomedicine is the overwhelming number
and diversity of complex images that require annotation, analysis and
interpretation. Recent advances in Deep Learning have revolutionized the field
of computer vision, creating algorithms that compete with human experts in
image segmentation tasks. Crucially however, these frameworks require large
human-annotated datasets for training and the resulting models are difficult to
interpret. In this study, we introduce Kartezio, a modular Cartesian Genetic
Programming based computational strategy that generates transparent and easily
interpretable image processing pipelines by iteratively assembling and
parameterizing computer vision functions. The pipelines thus generated exhibit
comparable precision to state-of-the-art Deep Learning approaches on instance
segmentation tasks, while requiring drastically smaller training datasets, a
feature which confers tremendous flexibility, speed, and functionality to this
approach. We also deployed Kartezio to solve semantic and instance segmentation
problems in four real-world Use Cases, and showcase its utility in imaging
contexts ranging from high-resolution microscopy to clinical pathology. By
successfully implementing Kartezio on a portfolio of images ranging from
subcellular structures to tumoral tissue, we demonstrated the flexibility,
robustness and practical utility of this fully explicable evolutionary designer
for semantic and instance segmentation.Comment: 36 pages, 6 main Figures. The Extended Data Movie is available at the
following link: https://www.youtube.com/watch?v=r74gdzb6hdA. The source code
is available on Github: https://github.com/KevinCortacero/Kartezi
EIGEN: Ecologically-Inspired GENetic Approach for Neural Network Structure Searching from Scratch
Designing the structure of neural networks is considered one of the most
challenging tasks in deep learning, especially when there is few prior
knowledge about the task domain. In this paper, we propose an
Ecologically-Inspired GENetic (EIGEN) approach that uses the concept of
succession, extinction, mimicry, and gene duplication to search neural network
structure from scratch with poorly initialized simple network and few
constraints forced during the evolution, as we assume no prior knowledge about
the task domain. Specifically, we first use primary succession to rapidly
evolve a population of poorly initialized neural network structures into a more
diverse population, followed by a secondary succession stage for fine-grained
searching based on the networks from the primary succession. Extinction is
applied in both stages to reduce computational cost. Mimicry is employed during
the entire evolution process to help the inferior networks imitate the behavior
of a superior network and gene duplication is utilized to duplicate the learned
blocks of novel structures, both of which help to find better network
structures. Experimental results show that our proposed approach can achieve
similar or better performance compared to the existing genetic approaches with
dramatically reduced computation cost. For example, the network discovered by
our approach on CIFAR-100 dataset achieves 78.1% test accuracy under 120 GPU
hours, compared to 77.0% test accuracy in more than 65, 536 GPU hours in [35].Comment: CVPR 201
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